Decoding and Engineering the Phytobiome Communication for Smart Agriculture

TL;DR

This paper explores how communication theory can be applied to understand and engineer the phytobiome for smart agriculture, integrating molecular signals, electrophysiological data, and AI to improve sustainability and efficiency.

Contribution

It introduces a multi-scale communication framework for the phytobiome and demonstrates its application in modeling signals and developing smart agricultural practices.

Findings

Proposed a holistic communication model for the phytobiome.

Demonstrated electrophysiological signal modeling through plant experiments.

Outlined applications like smart irrigation and targeted agrochemical delivery.

Abstract

Smart agriculture applications, integrating technologies like the Internet of Things and machine learning/artificial intelligence (ML/AI) into agriculture, hold promise to address modern challenges of rising food demand, environmental pollution, and water scarcity. Alongside the concept of the phytobiome, which defines the area including the plant, its environment, and associated organisms, and the recent emergence of molecular communication (MC), there exists an important opportunity to advance agricultural science and practice using communication theory. In this article, we motivate to use the communication engineering perspective for developing a holistic understanding of the phytobiome communication and bridge the gap between the phytobiome communication and smart agriculture. Firstly, an overview of phytobiome communication via molecular and electrophysiological signals is presented and a multi-scale framework modeling the phytobiome as a communication network is conceptualized. Then, how this framework is used to model electrophysiological signals is demonstrated with plant experiments. Furthermore, possible smart agriculture applications, such as smart irrigation and targeted delivery of agrochemicals, through engineering the phytobiome communication are proposed. These applications merge ML/AI methods with the Internet of Bio-Nano-Things enabled by MC and pave the way towards more efficient, sustainable, and eco-friendly agricultural production. Finally, the implementation challenges, open research issues, and industrial outlook for these applications are discussed.